Face recognition, as a convenient, natural, and widely applied emerging technology, has achieved many significant research results in recent years. 2D face recognition has drawn extensive studies, while previously,2D face recognition is too sensitive to variations in features like facial expressions. To avoid the shortcoming, more attention was paid to the optimization of algorithms, stronger computational capabilities, and fusion strategies, which contributed greatly to the accuracy of face recognition and made it more outstanding. Compared to existing methods, RGB-D images tend to be more robust and reliable. Based on different processing methods of RGB-D 3D face data, researchers have proposed numerous 3D face recognition methods, such as 3D reconstruction methods from monocular RGB-D images, methods based on point cloud data, and methods based on image depth map data. This paper focuses mainly on the image depth map data method, analyzing its rich development history and its unique advantages and disadvantages in RGB-D 3D face recognition. Additionally, we introduced some common RGB-D face datasets, analyzing data collection methods.
{"title":"3D face recognition based on RGB-D data: a survey","authors":"Junhao Liu","doi":"10.61173/9hh86v72","DOIUrl":"https://doi.org/10.61173/9hh86v72","url":null,"abstract":"Face recognition, as a convenient, natural, and widely applied emerging technology, has achieved many significant research results in recent years. 2D face recognition has drawn extensive studies, while previously,2D face recognition is too sensitive to variations in features like facial expressions. To avoid the shortcoming, more attention was paid to the optimization of algorithms, stronger computational capabilities, and fusion strategies, which contributed greatly to the accuracy of face recognition and made it more outstanding. Compared to existing methods, RGB-D images tend to be more robust and reliable. Based on different processing methods of RGB-D 3D face data, researchers have proposed numerous 3D face recognition methods, such as 3D reconstruction methods from monocular RGB-D images, methods based on point cloud data, and methods based on image depth map data. This paper focuses mainly on the image depth map data method, analyzing its rich development history and its unique advantages and disadvantages in RGB-D 3D face recognition. Additionally, we introduced some common RGB-D face datasets, analyzing data collection methods.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"33 S125","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper explores the wide range of applications of machine learning techniques in the field of cancer, with a particular focus on their specific use in the diagnosis and classification of important cancer types such as lung, oral and breast cancer. The paper concludes that machine learning algorithms can assist physicians in detecting cancerous lesions earlier and improve the accuracy of diagnosis. In addition, the paper explores the importance of machine learning in the early detection and treatment of cancer and its potential for collaboration with clinicians. In the future, collaborations across datasets and across healthcare institutions will drive further development of machine learning algorithms, providing more possibilities for personalized medical diagnosis and treatment plans to maximize patient survival and quality of life. The research in this paper can give relevant readers with insight into the potential and application of machine learning in the field of cancer, as well as its important role in improving the efficiency and quality of healthcare services.
{"title":"Exploring the Application of Machine Learning to Cancer Prediction","authors":"Xianwen Jiang","doi":"10.61173/81mmj896","DOIUrl":"https://doi.org/10.61173/81mmj896","url":null,"abstract":"This paper explores the wide range of applications of machine learning techniques in the field of cancer, with a particular focus on their specific use in the diagnosis and classification of important cancer types such as lung, oral and breast cancer. The paper concludes that machine learning algorithms can assist physicians in detecting cancerous lesions earlier and improve the accuracy of diagnosis. In addition, the paper explores the importance of machine learning in the early detection and treatment of cancer and its potential for collaboration with clinicians. In the future, collaborations across datasets and across healthcare institutions will drive further development of machine learning algorithms, providing more possibilities for personalized medical diagnosis and treatment plans to maximize patient survival and quality of life. The research in this paper can give relevant readers with insight into the potential and application of machine learning in the field of cancer, as well as its important role in improving the efficiency and quality of healthcare services.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"8 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bipolar disorder is a severe emotional disorder that causes significant damage to patients’ cognitive functions. Although the clinical manifestations of bipolar disorder are clear, its pathophysiological mechanisms are currently not well understood. The theme of this review is to explore the pathophysiological mechanisms of bipolar disorder. Starting from the lithium treatment mechanism, the author identified an important target - GSK-3, by reviewing previous literature. Research has shown that GSK-3 plays a crucial role in the bipolar disorder. This review provides an introduction to GSK-3 and its probable mechanisms that contribute to bipolar disease. It also examines the role of GSK-3 in bipolar disorder from both the standpoint of how it develops and how it might be treated. By studying GSK-3, we can augment our comprehension of bipolar disorder and further delve into our grasp of this condition.
{"title":"GSK3: The Shared Target of Circadian Rhythm and Lithium Treatment in Bipolar Disorder","authors":"Puyuan Ge","doi":"10.61173/b1xvkf28","DOIUrl":"https://doi.org/10.61173/b1xvkf28","url":null,"abstract":"Bipolar disorder is a severe emotional disorder that causes significant damage to patients’ cognitive functions. Although the clinical manifestations of bipolar disorder are clear, its pathophysiological mechanisms are currently not well understood. The theme of this review is to explore the pathophysiological mechanisms of bipolar disorder. Starting from the lithium treatment mechanism, the author identified an important target - GSK-3, by reviewing previous literature. Research has shown that GSK-3 plays a crucial role in the bipolar disorder. This review provides an introduction to GSK-3 and its probable mechanisms that contribute to bipolar disease. It also examines the role of GSK-3 in bipolar disorder from both the standpoint of how it develops and how it might be treated. By studying GSK-3, we can augment our comprehension of bipolar disorder and further delve into our grasp of this condition.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"9 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, the application status of graphene materials in wearable sensors was studied. Due to the excellent properties of graphene materials in mechanics, electricity, biocompatibility and other aspects, it has great hope to be used as wearable sensor materials, and play an important role in health detection, Internet of things and other fields. This paper summarizes the characteristics of graphene materials and the development and application of wearable sensors, expounds the advantages of graphene materials in wearable sensors, and introduces the application status of graphene materials in temperature sensing, heart rate monitoring and motion monitoring in detail. Then the bottleneck of graphene materials in wearable sensors and the problems to be solved are analyzed. Finally, the development prospect of graphene materials in wearable sensors is prospected, in order to provide some improvement ideas and future research directions. In conclusion, this paper provides an important reference for the further development of this field by studying the application status and bottleneck of graphene materials in wearable sensors.
{"title":"Application Progress and Research Status of graphene materials in wearable sensors","authors":"Sikun Chai","doi":"10.61173/b1mzk394","DOIUrl":"https://doi.org/10.61173/b1mzk394","url":null,"abstract":"In this paper, the application status of graphene materials in wearable sensors was studied. Due to the excellent properties of graphene materials in mechanics, electricity, biocompatibility and other aspects, it has great hope to be used as wearable sensor materials, and play an important role in health detection, Internet of things and other fields. This paper summarizes the characteristics of graphene materials and the development and application of wearable sensors, expounds the advantages of graphene materials in wearable sensors, and introduces the application status of graphene materials in temperature sensing, heart rate monitoring and motion monitoring in detail. Then the bottleneck of graphene materials in wearable sensors and the problems to be solved are analyzed. Finally, the development prospect of graphene materials in wearable sensors is prospected, in order to provide some improvement ideas and future research directions. In conclusion, this paper provides an important reference for the further development of this field by studying the application status and bottleneck of graphene materials in wearable sensors.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"72 S322","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rare earth luminescent complex materials, its unique 4f-4f electronic transition, which shows excellent luminous performance, especially the europium complex materials, with temperature dependent luminescent performance of europium complex can achieve high sensitivity, high efficiency of temperature sensing process, can be applied to environmental engineering, energy technology and other fields of temperature measurement and monitoring. This review introduces the research progress of europium complex in temperature luminescence sensing system, summarizes the complex material category and analyzes the influencing factors of temperature response sensitivity, further summarizes the vibration relaxation and energy transfer, and introduces the preparation of europium EVA composite complex and research results in crystal silicon solar cells, for the study of lanthanide metal complexes temperature sensing performance, in order to open the door to the practical application of such materials.
{"title":"Progress of rare earth europium complexes in the field of temperature luminescence sensing","authors":"Yiyang Zhang","doi":"10.61173/2dafgx49","DOIUrl":"https://doi.org/10.61173/2dafgx49","url":null,"abstract":"Rare earth luminescent complex materials, its unique 4f-4f electronic transition, which shows excellent luminous performance, especially the europium complex materials, with temperature dependent luminescent performance of europium complex can achieve high sensitivity, high efficiency of temperature sensing process, can be applied to environmental engineering, energy technology and other fields of temperature measurement and monitoring. This review introduces the research progress of europium complex in temperature luminescence sensing system, summarizes the complex material category and analyzes the influencing factors of temperature response sensitivity, further summarizes the vibration relaxation and energy transfer, and introduces the preparation of europium EVA composite complex and research results in crystal silicon solar cells, for the study of lanthanide metal complexes temperature sensing performance, in order to open the door to the practical application of such materials.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"48 S230","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper illustrates a comparison between the Hidden Markov Model and the Support Vector Machine, two important methodologies and tools, used in Natural Language Processing. Breaking down the model formulations of each, this paper first describes the mathematical motivations behind their applications in NLP. The mathematical motivations will be discussed through formulas, ideas, and examples. Then, this paper applies two real pre-established algorithms, one for each model, as examples to further rationalize their unique characteristics, similarities, and differences. These aspects will be broken down further into algorithmic efficiency, effectiveness, and other factors. Based on their performances analyzed through each factor, specific toolkits will be proposed, explained, and tested to optimize the test results, as the improving method. Some examples of toolkits include YamCha, TinySVM, etc. Overall, Name Entity Recognition involves different methodologies, and SVM and HMM, which represent two leading areas of NLP research, can best describe future trends and current situations.
{"title":"Support Vector Machine and Hidden Markov Model in Name Entity Recognition of Natural Language Processing","authors":"Jiaheng Li","doi":"10.61173/brgdky68","DOIUrl":"https://doi.org/10.61173/brgdky68","url":null,"abstract":"This paper illustrates a comparison between the Hidden Markov Model and the Support Vector Machine, two important methodologies and tools, used in Natural Language Processing. Breaking down the model formulations of each, this paper first describes the mathematical motivations behind their applications in NLP. The mathematical motivations will be discussed through formulas, ideas, and examples. Then, this paper applies two real pre-established algorithms, one for each model, as examples to further rationalize their unique characteristics, similarities, and differences. These aspects will be broken down further into algorithmic efficiency, effectiveness, and other factors. Based on their performances analyzed through each factor, specific toolkits will be proposed, explained, and tested to optimize the test results, as the improving method. Some examples of toolkits include YamCha, TinySVM, etc. Overall, Name Entity Recognition involves different methodologies, and SVM and HMM, which represent two leading areas of NLP research, can best describe future trends and current situations.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"30 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Early and precise cancer diagnosis is essential for enhancing the effectiveness of treatments. Traditional biopsy techniques, while reliable, are often time-consuming and economically inefficient. Furthermore, variations in diagnostic assessments among physicians introduce additional uncertainty in outcomes. This paper investigates the application of machine learning (ML) and deep learning (DL) methods to improve diagnostic accuracy and efficiency. It evaluates the advantages and disadvantages of feature-based versus image-based diagnostic approaches and introduces a new diagnostic workflow named AIStain. This workflow encompasses two pathways: one involving feature extraction followed by classical machine learning techniques, and the other using convolutional neural networks (CNNs) for deep learning analysis. Our analysis demonstrates that integrating machine learning can significantly enhance diagnostic speed, reduce costs, and improve consistency across evaluations without compromising accuracy. By leveraging advanced computational techniques, this approach aims to standardize cancer diagnostics and reduce the dependency on subjective human evaluation, potentially transforming cancer diagnosis practices.
{"title":"Deep Learning for Accurate, Efficient, Economical, and ConsistentCancer Diagnosis Compared to Traditional Biopsy","authors":"Jiahong Lin","doi":"10.61173/et1nrh25","DOIUrl":"https://doi.org/10.61173/et1nrh25","url":null,"abstract":"Early and precise cancer diagnosis is essential for enhancing the effectiveness of treatments. Traditional biopsy techniques, while reliable, are often time-consuming and economically inefficient. Furthermore, variations in diagnostic assessments among physicians introduce additional uncertainty in outcomes. This paper investigates the application of machine learning (ML) and deep learning (DL) methods to improve diagnostic accuracy and efficiency. It evaluates the advantages and disadvantages of feature-based versus image-based diagnostic approaches and introduces a new diagnostic workflow named AIStain. This workflow encompasses two pathways: one involving feature extraction followed by classical machine learning techniques, and the other using convolutional neural networks (CNNs) for deep learning analysis. Our analysis demonstrates that integrating machine learning can significantly enhance diagnostic speed, reduce costs, and improve consistency across evaluations without compromising accuracy. By leveraging advanced computational techniques, this approach aims to standardize cancer diagnostics and reduce the dependency on subjective human evaluation, potentially transforming cancer diagnosis practices.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"6 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141380534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article focuses on railways, and from the perspective of World Heritage sites, uses the Ovi Interactive Map to summarize the overview and construction difficulties along the Qinghai-Tibet Railway in China. We select railway linear heritage sites from around the world, conduct comparative research with the Qinghai-Tibet Railway in terms of geographical location, selection criteria, diversity and protection of natural and cultural landscapes along the line, and ultimately propose a sustainable development model for the Qinghai-Tibet Railway from the perspective of heritage tourism, and explore the possibility of applying for World Heritage on the Qinghai-Tibet Railway.
{"title":"Research on the Application Strategy and Sustainable Development of the Qinghai-Tibet Railway from the Perspective of World Heritage Sites","authors":"Yufei Wang","doi":"10.61173/xhs3c745","DOIUrl":"https://doi.org/10.61173/xhs3c745","url":null,"abstract":"This article focuses on railways, and from the perspective of World Heritage sites, uses the Ovi Interactive Map to summarize the overview and construction difficulties along the Qinghai-Tibet Railway in China. We select railway linear heritage sites from around the world, conduct comparative research with the Qinghai-Tibet Railway in terms of geographical location, selection criteria, diversity and protection of natural and cultural landscapes along the line, and ultimately propose a sustainable development model for the Qinghai-Tibet Railway from the perspective of heritage tourism, and explore the possibility of applying for World Heritage on the Qinghai-Tibet Railway.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"13 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Heart disease is one of the highest mortality rate diseases worldwide, with arrhythmias frequently serving as a trigger (such as cardiomyopathy) or a complication (such as coronary heart disease) for cardiovascular diseases. Therefore, it is crucial to monitor abnormalities in heart function through the early identification of deviations in heart rate variability (HRV). In modern medical systems, wearable real-time monitoring devices and artificial intelligence are commonly employed to generate electrocardiograms (ECGs) and analyze HRV data. The key to this application lies in making reasonable judgments of HRV data using data mining tools, including multiple linear regression, support vector machine, random forest, or long-short-term memory neural networks. However, these models fail to yield satisfactory results for cardiac rhythm monitoring. Consequently, the paper introduces an optimized hybrid ARIMA-GARCH model to enable heart disease detection and pathological diagnosis, playing a guiding role in personalized treatment and the tracking of the cardiovascular health status of monitored individuals. The proposed model combines data preprocessing using the one-sided Hodrick Prescott filter and parameter tuning based on partitioning-interpolation techniques and Fast Discrete Fourier Transform to fit and predict the RR interval time series. Experimental results indicate that our proposed model exhibits significant advantages in quantitative assessments compared to other models, as it effectively preserves the trend and accounts for high volatility in short-term forward prediction.
{"title":"An Optimized Hybrid ARIMA-GARCH Model Application on RR Interval Time Series Prediction for Heart Disease","authors":"Sicheng Shu","doi":"10.61173/frjpde45","DOIUrl":"https://doi.org/10.61173/frjpde45","url":null,"abstract":"Heart disease is one of the highest mortality rate diseases worldwide, with arrhythmias frequently serving as a trigger (such as cardiomyopathy) or a complication (such as coronary heart disease) for cardiovascular diseases. Therefore, it is crucial to monitor abnormalities in heart function through the early identification of deviations in heart rate variability (HRV). In modern medical systems, wearable real-time monitoring devices and artificial intelligence are commonly employed to generate electrocardiograms (ECGs) and analyze HRV data. The key to this application lies in making reasonable judgments of HRV data using data mining tools, including multiple linear regression, support vector machine, random forest, or long-short-term memory neural networks. However, these models fail to yield satisfactory results for cardiac rhythm monitoring. Consequently, the paper introduces an optimized hybrid ARIMA-GARCH model to enable heart disease detection and pathological diagnosis, playing a guiding role in personalized treatment and the tracking of the cardiovascular health status of monitored individuals. The proposed model combines data preprocessing using the one-sided Hodrick Prescott filter and parameter tuning based on partitioning-interpolation techniques and Fast Discrete Fourier Transform to fit and predict the RR interval time series. Experimental results indicate that our proposed model exhibits significant advantages in quantitative assessments compared to other models, as it effectively preserves the trend and accounts for high volatility in short-term forward prediction.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"28 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the advancement of science and technology, electric vehicles have become increasingly prevalent as a mode of transportation. The vehicle is equipped with both wired and wireless charging capabilities. However, the latter requires a larger area, which restricts the number of charging opportunities and is incompatible with the growing demand for charging. Consequently, wireless charging will become the dominant method of charging electric vehicles in the future. This study will provide a comprehensive comparison of five main radio power transmission methods, with a particular focus on the in-depth analysis of ICPT and MCRPT systems. It will also discuss strategies to improve the efficiency of the magnetic coupling mechanism and cope with power fluctuations in dynamic charging. Additionally, different power supply modes will be evaluated, with particular emphasis on the efficiency, stability, and cost-effectiveness of the compensated rail supply mode. Finally, this study sought to identify the key research areas for the advancement of wireless charging technology for new energy electric vehicles. It also highlighted the areas that require particular attention in future research.
{"title":"Research review on wireless charging technology of new energy electric vehicles","authors":"Zhenghao Yu","doi":"10.61173/hh301154","DOIUrl":"https://doi.org/10.61173/hh301154","url":null,"abstract":"With the advancement of science and technology, electric vehicles have become increasingly prevalent as a mode of transportation. The vehicle is equipped with both wired and wireless charging capabilities. However, the latter requires a larger area, which restricts the number of charging opportunities and is incompatible with the growing demand for charging. Consequently, wireless charging will become the dominant method of charging electric vehicles in the future. This study will provide a comprehensive comparison of five main radio power transmission methods, with a particular focus on the in-depth analysis of ICPT and MCRPT systems. It will also discuss strategies to improve the efficiency of the magnetic coupling mechanism and cope with power fluctuations in dynamic charging. Additionally, different power supply modes will be evaluated, with particular emphasis on the efficiency, stability, and cost-effectiveness of the compensated rail supply mode. Finally, this study sought to identify the key research areas for the advancement of wireless charging technology for new energy electric vehicles. It also highlighted the areas that require particular attention in future research.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"102 4‐7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141381300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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